bsam 1.1.3

fixes an error caused by recent updates to the dplyr package. See NEWS.md for additional details

bsam 1.1.2

• fixed indexing on priors for initial location state. A sufficiently short time step could cause the hierarchical models to crash on compilation.

v1.1.1

bsam 1.1.1

Fixes

• fixed the mis-ordering of animal id's in the summary and data output data.frames caused when a hierarchical model was fit to a dataset with >= 10 individuals

v1.1.0

bsam 1.1.0

Fixes

• major bug fix - data indexing when fitting hierarchical models was causing improper fits to multi-individual datasets with > 2 individuals. Indexing now works as intended
• Prevented individual animal tracks from being re-ordered by split function so output track order now matches input track order

New Features

• get_summary function to extract summary data_frame from fit_ssm output objects. The data_frame can optionally be written to a .csv file
• list of initial values now included in the output list object. Making these available can aid diagnosing lack of convergence and inform choice of span argument value for generating location state initial values
• sim function to simulate tracks from the DCRW model with Argos errors

Misc

• added BugReports URL to package Description

v1.0.0

• Simplified movement models by removing the mean turn angle parameter. This tends to improve convergence for the behavioural
  switching models.
• Simplified the regularisation / interpolation in the observation models.
• Simplified data preparation code.
• Diagnostic plots (renamed from diagSSM to diag_ssm) now include the Brooks-Gelman-Rubin shrink factor plots for each parameter.
• New mapping function (map_ssm) uses coastline data from rworldxtra and ggplot2 for core plotting functions.
• New plot function (plot_fit) to inspect fit to location data.
• Renamed core function fitSSM to fit_ssm.
• Improved selection of random initial values for MCMC sampling.
• Initial values for location states are now based on a loess smooth through the observed locations. Users can control the degree of smoothing via the span argument to fit_ssm.